Overview

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.1 KiB
Average record size in memory88.1 B

Variable types

Numeric10
Categorical1

Warnings

target is uniformly distributed Uniform
X0 has unique values Unique
X1 has unique values Unique
X2 has unique values Unique
X3 has unique values Unique
X4 has unique values Unique
X5 has unique values Unique
X6 has unique values Unique
X7 has unique values Unique
X8 has unique values Unique
X9 has unique values Unique

Reproduction

Analysis started2021-02-12 21:16:38.762686
Analysis finished2021-02-12 21:29:02.909718
Duration12 minutes and 24.15 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

X0
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.02113462003
Minimum-3.101943429
Maximum3.9804864
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T16:29:10.657914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.101943429
5-th percentile-1.65110912
Q1-0.7295546458
median-0.01758951762
Q30.6758477704
95-th percentile1.700163051
Maximum3.9804864
Range7.082429828
Interquartile range (IQR)1.405402416

Descriptive statistics

Standard deviation1.017368953
Coefficient of variation (CV)-48.13755587
Kurtosis-0.09436837565
Mean-0.02113462003
Median Absolute Deviation (MAD)0.7028924116
Skewness0.07401155113
Sum-21.13462003
Variance1.035039586
MonotocityNot monotonic
2021-02-12T16:29:18.935991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.99975949871
 
0.1%
0.85766626681
 
0.1%
0.32639914151
 
0.1%
-0.035541551621
 
0.1%
1.1434037981
 
0.1%
1.3465431361
 
0.1%
-1.2684527181
 
0.1%
-0.49654072861
 
0.1%
-0.2106326921
 
0.1%
0.46596712661
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.1019434291
0.1%
-2.9394371021
0.1%
-2.777289811
0.1%
-2.4412498661
0.1%
-2.3793339091
0.1%
-2.3755361151
0.1%
-2.3143195331
0.1%
-2.2857398761
0.1%
-2.2769124071
0.1%
-2.2554545231
0.1%
ValueCountFrequency (%)
3.98048641
0.1%
2.7529791861
0.1%
2.5834544031
0.1%
2.4208807551
0.1%
2.4049423251
0.1%
2.3748587361
0.1%
2.3660747291
0.1%
2.3645834391
0.1%
2.3221322951
0.1%
2.2979555431
0.1%

X1
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03040330541
Minimum-3.420414787
Maximum3.218321253
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T16:29:27.239109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.420414787
5-th percentile-1.613259366
Q1-0.6681997071
median0.02030752657
Q30.7046063495
95-th percentile1.742216691
Maximum3.218321253
Range6.638736041
Interquartile range (IQR)1.372806057

Descriptive statistics

Standard deviation1.022449975
Coefficient of variation (CV)33.6295663
Kurtosis-0.08525727659
Mean0.03040330541
Median Absolute Deviation (MAD)0.6868167006
Skewness0.1100903737
Sum30.40330541
Variance1.045403952
MonotocityNot monotonic
2021-02-12T16:29:35.742670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.38838900311
 
0.1%
0.11790105751
 
0.1%
-1.5622703481
 
0.1%
0.94461811841
 
0.1%
-0.46795586261
 
0.1%
-0.72740088621
 
0.1%
0.38277334371
 
0.1%
-0.71504669861
 
0.1%
-0.96050805051
 
0.1%
0.30347703961
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.4204147871
0.1%
-2.9137932911
0.1%
-2.7435683241
0.1%
-2.5586300831
0.1%
-2.535166751
0.1%
-2.4258354911
0.1%
-2.3520087591
0.1%
-2.3392788661
0.1%
-2.2670041231
0.1%
-2.256651841
0.1%
ValueCountFrequency (%)
3.2183212531
0.1%
2.9824424891
0.1%
2.8929247551
0.1%
2.8792732791
0.1%
2.6956689551
0.1%
2.6284184861
0.1%
2.5540534421
0.1%
2.5168075661
0.1%
2.5065004641
0.1%
2.4975201051
0.1%

X2
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.03323446155
Minimum-3.23053491
Maximum4.76914171
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T16:29:43.673943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.23053491
5-th percentile-1.696856766
Q1-0.6984543563
median-0.061865209
Q30.635935078
95-th percentile1.674054687
Maximum4.76914171
Range7.99967662
Interquartile range (IQR)1.334389434

Descriptive statistics

Standard deviation1.017723604
Coefficient of variation (CV)-30.62253927
Kurtosis0.4762767212
Mean-0.03323446155
Median Absolute Deviation (MAD)0.6490584382
Skewness0.1349539954
Sum-33.23446155
Variance1.035761334
MonotocityNot monotonic
2021-02-12T16:29:51.641959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.12312966351
 
0.1%
-1.8795387781
 
0.1%
-0.23591382261
 
0.1%
-0.90767066971
 
0.1%
1.7602349661
 
0.1%
-0.16621232971
 
0.1%
-0.59953713971
 
0.1%
0.91509293561
 
0.1%
-2.3654735471
 
0.1%
-1.6283239331
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.230534911
0.1%
-3.1074429461
0.1%
-2.9093842321
0.1%
-2.8882580651
0.1%
-2.6651445391
0.1%
-2.5911134791
0.1%
-2.5862919491
0.1%
-2.5815418881
0.1%
-2.4758974721
0.1%
-2.4587701331
0.1%
ValueCountFrequency (%)
4.769141711
0.1%
3.3383649561
0.1%
3.1007709441
0.1%
2.8888741471
0.1%
2.6132159491
0.1%
2.4643936261
0.1%
2.4471031561
0.1%
2.4350467471
0.1%
2.4225453321
0.1%
2.3693027831
0.1%

X3
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.01458635947
Minimum-3.471662685
Maximum3.119329552
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T16:29:59.830573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.471662685
5-th percentile-1.625115315
Q1-0.6889513817
median0.002358642728
Q30.7048869708
95-th percentile1.582289847
Maximum3.119329552
Range6.590992237
Interquartile range (IQR)1.393838353

Descriptive statistics

Standard deviation1.003209939
Coefficient of variation (CV)-68.77726689
Kurtosis0.04454858315
Mean-0.01458635947
Median Absolute Deviation (MAD)0.6991034087
Skewness-0.09696659779
Sum-14.58635947
Variance1.006430181
MonotocityNot monotonic
2021-02-12T16:30:08.077218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.79075169011
 
0.1%
-0.25035794381
 
0.1%
-0.52348358151
 
0.1%
-0.68400774741
 
0.1%
1.1576184461
 
0.1%
-1.5307445391
 
0.1%
0.80799361771
 
0.1%
1.4952812221
 
0.1%
1.0611801181
 
0.1%
-1.3042202741
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.4716626851
0.1%
-3.2869298581
0.1%
-3.1611791811
0.1%
-3.0459948221
0.1%
-2.9490433151
0.1%
-2.7355276751
0.1%
-2.4813930061
0.1%
-2.4762695731
0.1%
-2.4725788021
0.1%
-2.4397556341
0.1%
ValueCountFrequency (%)
3.1193295521
0.1%
3.052546411
0.1%
2.7139393631
0.1%
2.5935647761
0.1%
2.5597179421
0.1%
2.5457872341
0.1%
2.4352952251
0.1%
2.3208324111
0.1%
2.3054097181
0.1%
2.2777726021
0.1%

X4
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02361349263
Minimum-3.655361146
Maximum3.378980504
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T16:30:16.093218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.655361146
5-th percentile-1.553843113
Q1-0.6850631537
median0.01128554615
Q30.6800216736
95-th percentile1.7089238
Maximum3.378980504
Range7.03434165
Interquartile range (IQR)1.365084827

Descriptive statistics

Standard deviation1.004365373
Coefficient of variation (CV)42.53353746
Kurtosis0.03042381456
Mean0.02361349263
Median Absolute Deviation (MAD)0.6774161584
Skewness0.08283672148
Sum23.61349263
Variance1.008749803
MonotocityNot monotonic
2021-02-12T16:30:24.360511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.4201523381
 
0.1%
-2.1582208691
 
0.1%
0.31949298121
 
0.1%
1.0822636731
 
0.1%
-0.47293742031
 
0.1%
-0.72123268021
 
0.1%
0.19782708761
 
0.1%
-1.1051316551
 
0.1%
-0.53572607681
 
0.1%
-2.5236089641
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.6553611461
0.1%
-3.0463166071
0.1%
-2.7201730271
0.1%
-2.5236089641
0.1%
-2.5046454121
0.1%
-2.4497821731
0.1%
-2.3891854971
0.1%
-2.2896776291
0.1%
-2.1582208691
0.1%
-2.1538087221
0.1%
ValueCountFrequency (%)
3.3789805041
0.1%
3.2273534671
0.1%
3.2204663891
0.1%
3.0153603581
0.1%
2.9709948251
0.1%
2.7809532021
0.1%
2.4604228941
0.1%
2.3646578081
0.1%
2.3288269491
0.1%
2.2981092571
0.1%

X5
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.002427773267
Minimum-3.963316839
Maximum3.250227164
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T16:30:32.417646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.963316839
5-th percentile-1.738223301
Q1-0.6989933173
median-0.02167925336
Q30.6955249129
95-th percentile1.650791518
Maximum3.250227164
Range7.213544003
Interquartile range (IQR)1.39451823

Descriptive statistics

Standard deviation1.034584995
Coefficient of variation (CV)-426.145641
Kurtosis0.1128285635
Mean-0.002427773267
Median Absolute Deviation (MAD)0.6877608864
Skewness-0.04302874284
Sum-2.427773267
Variance1.070366112
MonotocityNot monotonic
2021-02-12T16:30:40.608820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.94675287481
 
0.1%
1.5718981411
 
0.1%
-1.0765732321
 
0.1%
1.1693354151
 
0.1%
0.080330309421
 
0.1%
0.82003813381
 
0.1%
-1.3399134751
 
0.1%
1.0423482051
 
0.1%
0.26341579331
 
0.1%
-0.13837669111
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.9633168391
0.1%
-3.2613037521
0.1%
-3.1333117121
0.1%
-2.8354138021
0.1%
-2.7786819491
0.1%
-2.7375691721
0.1%
-2.6340062791
0.1%
-2.5851186941
0.1%
-2.5385139961
0.1%
-2.5064985751
0.1%
ValueCountFrequency (%)
3.2502271641
0.1%
3.2325124421
0.1%
3.0838768431
0.1%
2.7160839411
0.1%
2.6337489521
0.1%
2.5620593981
0.1%
2.5387484211
0.1%
2.4985279141
0.1%
2.4705857191
0.1%
2.4586655921
0.1%

X6
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.01650603921
Minimum-3.128558288
Maximum2.772822571
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T16:30:48.510438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.128558288
5-th percentile-1.630543667
Q1-0.6924105453
median-0.04349848454
Q30.6288863056
95-th percentile1.681672575
Maximum2.772822571
Range5.901380858
Interquartile range (IQR)1.321296851

Descriptive statistics

Standard deviation1.002963814
Coefficient of variation (CV)-60.76344547
Kurtosis0.001863894444
Mean-0.01650603921
Median Absolute Deviation (MAD)0.6608088418
Skewness0.0210420861
Sum-16.50603921
Variance1.005936411
MonotocityNot monotonic
2021-02-12T16:30:56.601041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.57052692991
 
0.1%
-0.25787100541
 
0.1%
-1.6541397041
 
0.1%
-1.3918949851
 
0.1%
0.031294448351
 
0.1%
-0.74165435311
 
0.1%
1.3129712191
 
0.1%
0.40826825691
 
0.1%
-0.13477986851
 
0.1%
1.4244052931
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.1285582881
0.1%
-2.9220364151
0.1%
-2.86458571
0.1%
-2.8437410181
0.1%
-2.8228185081
0.1%
-2.7195440711
0.1%
-2.6817529611
0.1%
-2.5624371781
0.1%
-2.4965304941
0.1%
-2.4173561321
0.1%
ValueCountFrequency (%)
2.7728225711
0.1%
2.5835418891
0.1%
2.5802378951
0.1%
2.5779942321
0.1%
2.5254666221
0.1%
2.5142731681
0.1%
2.4941432421
0.1%
2.4461579691
0.1%
2.3725251861
0.1%
2.3628097331
0.1%

X7
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.02240501689
Minimum-2.7622976
Maximum3.663236173
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T16:31:04.820184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.7622976
5-th percentile-1.699595298
Q1-0.7286942039
median-0.02018385422
Q30.6452249605
95-th percentile1.646136187
Maximum3.663236173
Range6.425533773
Interquartile range (IQR)1.373919164

Descriptive statistics

Standard deviation1.011856076
Coefficient of variation (CV)-45.16203136
Kurtosis0.04470727634
Mean-0.02240501689
Median Absolute Deviation (MAD)0.6953017256
Skewness0.08850181169
Sum-22.40501689
Variance1.023852718
MonotocityNot monotonic
2021-02-12T16:31:13.075057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.2785230971
 
0.1%
0.30210440531
 
0.1%
0.78002978441
 
0.1%
-0.50484274011
 
0.1%
0.85307466971
 
0.1%
1.3993622311
 
0.1%
1.7053218161
 
0.1%
-0.02486108751
 
0.1%
-0.60430132281
 
0.1%
0.28965523691
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.76229761
0.1%
-2.6099279841
0.1%
-2.5244049571
0.1%
-2.5080354211
0.1%
-2.4718436121
0.1%
-2.428583861
0.1%
-2.4221310761
0.1%
-2.3884224141
0.1%
-2.3752272871
0.1%
-2.3741739131
0.1%
ValueCountFrequency (%)
3.6632361731
0.1%
3.2900580521
0.1%
3.0145221761
0.1%
2.7464704741
0.1%
2.7388055381
0.1%
2.684466611
0.1%
2.6373903551
0.1%
2.6054324171
0.1%
2.4947602571
0.1%
2.4533540551
0.1%

X8
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01829336955
Minimum-4.296000305
Maximum3.303361055
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T16:31:21.196905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-4.296000305
5-th percentile-1.59478042
Q1-0.6632969899
median0.04550915855
Q30.6702989167
95-th percentile1.679901214
Maximum3.303361055
Range7.59936136
Interquartile range (IQR)1.333595907

Descriptive statistics

Standard deviation1.009014027
Coefficient of variation (CV)55.15736315
Kurtosis0.4179497104
Mean0.01829336955
Median Absolute Deviation (MAD)0.6708420749
Skewness-0.11548628
Sum18.29336955
Variance1.018109307
MonotocityNot monotonic
2021-02-12T16:31:29.490631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.21166320241
 
0.1%
0.60302598581
 
0.1%
0.6421343921
 
0.1%
0.7594338451
 
0.1%
2.4232047311
 
0.1%
-0.97506008851
 
0.1%
-0.28957817611
 
0.1%
0.61989300951
 
0.1%
-0.63148439471
 
0.1%
-0.014487982011
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-4.2960003051
0.1%
-3.1086631481
0.1%
-3.0159411671
0.1%
-2.9448471551
0.1%
-2.9202076281
0.1%
-2.9040105281
0.1%
-2.8334928581
0.1%
-2.7457568511
0.1%
-2.6727276271
0.1%
-2.586368231
0.1%
ValueCountFrequency (%)
3.3033610551
0.1%
3.0351636941
0.1%
2.8065458691
0.1%
2.7712587131
0.1%
2.6022184851
0.1%
2.4601187861
0.1%
2.4572598371
0.1%
2.4531285841
0.1%
2.4428451931
0.1%
2.4232047311
0.1%

X9
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.05265586913
Minimum-3.142946592
Maximum3.41069214
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T16:31:37.887230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.142946592
5-th percentile-1.561619455
Q1-0.7819918156
median-0.09402188208
Q30.6518523627
95-th percentile1.508102588
Maximum3.41069214
Range6.553638732
Interquartile range (IQR)1.433844178

Descriptive statistics

Standard deviation0.9838371473
Coefficient of variation (CV)-18.68428275
Kurtosis-0.2759341216
Mean-0.05265586913
Median Absolute Deviation (MAD)0.7177343523
Skewness0.1297740331
Sum-52.65586913
Variance0.9679355325
MonotocityNot monotonic
2021-02-12T16:31:45.932250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.58594089881
 
0.1%
1.6800980841
 
0.1%
0.031235175711
 
0.1%
-0.028998846131
 
0.1%
-1.4898205071
 
0.1%
-0.57583085491
 
0.1%
0.34545447561
 
0.1%
0.38429100611
 
0.1%
-0.37827615971
 
0.1%
-1.3676412821
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.1429465921
0.1%
-2.9289270141
0.1%
-2.5278198851
0.1%
-2.4470717681
0.1%
-2.2946841831
0.1%
-2.2765479891
0.1%
-2.2689742971
0.1%
-2.2177428221
0.1%
-2.214974781
0.1%
-2.1264545681
0.1%
ValueCountFrequency (%)
3.410692141
0.1%
2.8105376961
0.1%
2.6352132231
0.1%
2.6225571091
0.1%
2.617513851
0.1%
2.6024363851
0.1%
2.597970681
0.1%
2.5558543821
0.1%
2.5000254931
0.1%
2.3570269051
0.1%

target
Categorical

UNIFORM

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
500 
1
500 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0
ValueCountFrequency (%)
0500
50.0%
1500
50.0%
2021-02-12T16:32:02.494580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T16:32:10.674344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring characters

ValueCountFrequency (%)
0500
50.0%
1500
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

ValueCountFrequency (%)
0500
50.0%
1500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

ValueCountFrequency (%)
0500
50.0%
1500
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ValueCountFrequency (%)
0500
50.0%
1500
50.0%

Interactions

2021-02-12T16:16:47.768644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:16:55.575357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:17:03.208200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:17:11.115850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:17:18.712428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:17:26.485543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:17:34.783572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:17:42.587146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:17:50.359641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:17:58.287695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:18:06.123095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:18:14.123708image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:18:21.943673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:18:29.992029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:18:37.768255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:18:45.374792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:18:54.203074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:19:01.818273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:19:09.945469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:19:17.695844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:19:25.436117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:19:34.001201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:19:42.062510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:19:49.802551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:19:57.949834image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:20:05.735192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:20:15.057588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:20:22.843314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:20:31.830428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:20:39.958337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:20:48.035601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:20:55.924542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:21:03.702923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:21:11.719190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:21:19.648358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:21:27.416003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:21:35.775807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:21:43.807818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:21:51.720102image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:21:59.370008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:22:07.155973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:22:15.176387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:22:22.830585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:22:30.931882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:22:38.486073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:22:45.796004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:22:54.116503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:23:02.398786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:23:10.767078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:23:18.783465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:23:27.203557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:23:36.735515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:23:45.395040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:23:53.382463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:24:01.495341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:24:10.828161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:24:18.775934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:24:26.654930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:24:34.748194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:24:42.898367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:24:50.586401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:24:58.742736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:25:06.826168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:25:14.623783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:25:22.391615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:25:30.148132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:25:38.263566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:25:45.958848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:25:53.671802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:26:01.584082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:26:09.591944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:26:17.395586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:26:25.195298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:26:33.166708image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:26:40.859783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:26:49.010387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:26:57.429550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:27:05.655443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:27:13.746482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:27:21.738838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:27:29.974575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:27:38.195551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:27:46.365946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:27:54.508082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:28:03.167762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:28:11.194906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:28:19.207350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:28:27.335315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:28:35.518751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T16:28:44.119503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-12T16:32:18.739055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-12T16:32:27.131479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-12T16:32:35.398767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-12T16:32:43.705259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-02-12T16:28:52.965089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-12T16:29:02.497795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

X0X1X2X3X4X5X6X7X8X9target
00.2441650.671678-0.981272-0.1058320.5055850.2847181.7693880.850968-0.4463320.6673100
1-0.338551-1.648644-0.783602-0.206145-0.782583-0.217632-1.6422640.5125781.841412-1.6365120
2-0.1017970.5638280.933453-0.163425-1.288084-3.1333120.901355-0.024766-0.663102-0.8109491
31.395939-0.7992140.6938200.4508320.863686-1.4175110.2426590.474570-0.527671-0.5814830
4-0.3681780.079682-0.9791890.0388240.340670-0.8467201.565344-0.8023101.2327290.9039960
50.090061-0.9292702.3521170.439712-0.080140-0.449586-0.0418630.5928900.3645571.5435151
60.1921401.357878-1.0657770.5899920.274634-0.3787530.8231180.1959851.225544-0.3684930
7-0.410979-0.034065-0.9063440.272399-0.159630-1.390954-1.5217831.076237-1.355209-1.1083081
80.818776-0.7366731.0016691.305645-0.801475-0.466737-0.9562790.899179-0.6517820.0187101
9-0.1940260.6614060.915093-0.0866480.9474410.727631-1.166098-0.2353690.296386-0.7728681

Last rows

X0X1X2X3X4X5X6X7X8X9target
9900.0488611.2548330.711088-1.6249711.221716-0.863969-0.736390-0.9409220.3928511.7955830
991-2.3793340.8648660.299809-0.6563030.974378-1.7876541.681506-0.1416770.0506831.5853231
992-0.281867-0.0558000.672520-0.643495-0.210631-2.259465-0.627721-0.041332-0.225954-0.4773150
993-2.221890-0.5102510.109621-0.9355130.861756-1.468547-0.3069180.090235-0.602405-1.5599330
9940.548870-0.063427-1.117859-0.614886-0.517328-0.762765-0.280830-1.5202770.5277860.0049450
9951.347803-0.049230-2.206185-0.3939480.1150890.018963-0.128805-0.9533460.1586950.7090361
9960.1317370.9344440.5492430.829845-0.3997400.068838-0.5502100.1371581.128395-1.0945970
997-1.028189-0.7573081.329476-0.886736-1.2120221.4058472.5835421.7224330.2628200.4718620
9980.6388071.1718020.7924580.0244120.705970-0.0868840.8156151.142470-1.798205-0.8838050
9991.3170810.2116600.918779-1.0595290.8542800.2142011.059182-0.2945280.8427660.3686180